{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import necessary dependencies and settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import scipy.stats as spstats\n",
    "\n",
    "%matplotlib inline\n",
    "mpl.style.reload_library()\n",
    "mpl.style.use('classic')\n",
    "mpl.rcParams['figure.facecolor'] = (1, 1, 1, 0)\n",
    "mpl.rcParams['figure.figsize'] = [6.0, 4.0]\n",
    "mpl.rcParams['figure.dpi'] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Raw Measures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>Gen 1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>Gen 1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>Gen 1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>Gen 1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>Gen 1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #                   Name Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "0  1              Bulbasaur  Grass  Poison    318  45      49       49   \n",
       "1  2                Ivysaur  Grass  Poison    405  60      62       63   \n",
       "2  3               Venusaur  Grass  Poison    525  80      82       83   \n",
       "3  3  VenusaurMega Venusaur  Grass  Poison    625  80     100      123   \n",
       "4  4             Charmander   Fire     NaN    309  39      52       43   \n",
       "\n",
       "   Sp. Atk  Sp. Def  Speed Generation  Legendary  \n",
       "0       65       65     45      Gen 1      False  \n",
       "1       80       80     60      Gen 1      False  \n",
       "2      100      100     80      Gen 1      False  \n",
       "3      122      120     80      Gen 1      False  \n",
       "4       60       50     65      Gen 1      False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_df = pd.read_csv('datasets/Pokemon.csv', encoding='utf-8')\n",
    "poke_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>45</td>\n",
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       "      <th>1</th>\n",
       "      <td>60</td>\n",
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       "      <td>63</td>\n",
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       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
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       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   HP  Attack  Defense\n",
       "0  45      49       49\n",
       "1  60      62       63\n",
       "2  80      82       83\n",
       "3  80     100      123\n",
       "4  39      52       43"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_df[['HP', 'Attack', 'Defense']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Defense</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>69.258750</td>\n",
       "      <td>79.001250</td>\n",
       "      <td>73.842500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25.534669</td>\n",
       "      <td>32.457366</td>\n",
       "      <td>31.183501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>255.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>230.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               HP      Attack     Defense\n",
       "count  800.000000  800.000000  800.000000\n",
       "mean    69.258750   79.001250   73.842500\n",
       "std     25.534669   32.457366   31.183501\n",
       "min      1.000000    5.000000    5.000000\n",
       "25%     50.000000   55.000000   50.000000\n",
       "50%     65.000000   75.000000   70.000000\n",
       "75%     80.000000  100.000000   90.000000\n",
       "max    255.000000  190.000000  230.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_df[['HP', 'Attack', 'Defense']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>song_id</th>\n",
       "      <th>title</th>\n",
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       "      <th>9</th>\n",
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      ],
      "text/plain": [
       "                                    user_id             song_id  \\\n",
       "0  b6b799f34a204bd928ea014c243ddad6d0be4f8f  SOBONKR12A58A7A7E0   \n",
       "1  b41ead730ac14f6b6717b9cf8859d5579f3f8d4d  SOBONKR12A58A7A7E0   \n",
       "2  4c84359a164b161496d05282707cecbd50adbfc4  SOBONKR12A58A7A7E0   \n",
       "3  779b5908593756abb6ff7586177c966022668b06  SOBONKR12A58A7A7E0   \n",
       "4  dd88ea94f605a63d9fc37a214127e3f00e85e42d  SOBONKR12A58A7A7E0   \n",
       "5  68f0359a2f1cedb0d15c98d88017281db79f9bc6  SOBONKR12A58A7A7E0   \n",
       "6  116a4c95d63623a967edf2f3456c90ebbf964e6f  SOBONKR12A58A7A7E0   \n",
       "7  45544491ccfcdc0b0803c34f201a6287ed4e30f8  SOBONKR12A58A7A7E0   \n",
       "8  e701a24d9b6c59f5ac37ab28462ca82470e27cfb  SOBONKR12A58A7A7E0   \n",
       "9  edc8b7b1fd592a3b69c3d823a742e1a064abec95  SOBONKR12A58A7A7E0   \n",
       "\n",
       "            title  listen_count  \n",
       "0  You're The One             2  \n",
       "1  You're The One             0  \n",
       "2  You're The One             0  \n",
       "3  You're The One             0  \n",
       "4  You're The One             0  \n",
       "5  You're The One             0  \n",
       "6  You're The One            17  \n",
       "7  You're The One             0  \n",
       "8  You're The One            68  \n",
       "9  You're The One             0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popsong_df = pd.read_csv('datasets/song_views.csv', encoding='utf-8')\n",
    "popsong_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binarization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>You're The One</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dd88ea94f605a63d9fc37a214127e3f00e85e42d</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>You're The One</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68f0359a2f1cedb0d15c98d88017281db79f9bc6</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>You're The One</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>116a4c95d63623a967edf2f3456c90ebbf964e6f</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>You're The One</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>45544491ccfcdc0b0803c34f201a6287ed4e30f8</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>You're The One</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>e701a24d9b6c59f5ac37ab28462ca82470e27cfb</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>You're The One</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>edc8b7b1fd592a3b69c3d823a742e1a064abec95</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>You're The One</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    user_id             song_id  \\\n",
       "0  b6b799f34a204bd928ea014c243ddad6d0be4f8f  SOBONKR12A58A7A7E0   \n",
       "1  b41ead730ac14f6b6717b9cf8859d5579f3f8d4d  SOBONKR12A58A7A7E0   \n",
       "2  4c84359a164b161496d05282707cecbd50adbfc4  SOBONKR12A58A7A7E0   \n",
       "3  779b5908593756abb6ff7586177c966022668b06  SOBONKR12A58A7A7E0   \n",
       "4  dd88ea94f605a63d9fc37a214127e3f00e85e42d  SOBONKR12A58A7A7E0   \n",
       "5  68f0359a2f1cedb0d15c98d88017281db79f9bc6  SOBONKR12A58A7A7E0   \n",
       "6  116a4c95d63623a967edf2f3456c90ebbf964e6f  SOBONKR12A58A7A7E0   \n",
       "7  45544491ccfcdc0b0803c34f201a6287ed4e30f8  SOBONKR12A58A7A7E0   \n",
       "8  e701a24d9b6c59f5ac37ab28462ca82470e27cfb  SOBONKR12A58A7A7E0   \n",
       "9  edc8b7b1fd592a3b69c3d823a742e1a064abec95  SOBONKR12A58A7A7E0   \n",
       "\n",
       "            title  listen_count  watched  \n",
       "0  You're The One             2        1  \n",
       "1  You're The One             0        0  \n",
       "2  You're The One             0        0  \n",
       "3  You're The One             0        0  \n",
       "4  You're The One             0        0  \n",
       "5  You're The One             0        0  \n",
       "6  You're The One            17        1  \n",
       "7  You're The One             0        0  \n",
       "8  You're The One            68        1  \n",
       "9  You're The One             0        0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "watched = np.array(popsong_df['listen_count']) \n",
    "watched[watched >= 1] = 1\n",
    "popsong_df['watched'] = watched\n",
    "popsong_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>song_id</th>\n",
       "      <th>title</th>\n",
       "      <th>listen_count</th>\n",
       "      <th>watched</th>\n",
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       "      <td>SOBONKR12A58A7A7E0</td>\n",
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       "      <td>779b5908593756abb6ff7586177c966022668b06</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68f0359a2f1cedb0d15c98d88017281db79f9bc6</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>You're The One</td>\n",
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       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>You're The One</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>45544491ccfcdc0b0803c34f201a6287ed4e30f8</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>e701a24d9b6c59f5ac37ab28462ca82470e27cfb</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>You're The One</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>edc8b7b1fd592a3b69c3d823a742e1a064abec95</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>You're The One</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fb41d1c374d093ab643ef3bcd70eeb258d479076</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>You're The One</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     user_id             song_id  \\\n",
       "0   b6b799f34a204bd928ea014c243ddad6d0be4f8f  SOBONKR12A58A7A7E0   \n",
       "1   b41ead730ac14f6b6717b9cf8859d5579f3f8d4d  SOBONKR12A58A7A7E0   \n",
       "2   4c84359a164b161496d05282707cecbd50adbfc4  SOBONKR12A58A7A7E0   \n",
       "3   779b5908593756abb6ff7586177c966022668b06  SOBONKR12A58A7A7E0   \n",
       "4   dd88ea94f605a63d9fc37a214127e3f00e85e42d  SOBONKR12A58A7A7E0   \n",
       "5   68f0359a2f1cedb0d15c98d88017281db79f9bc6  SOBONKR12A58A7A7E0   \n",
       "6   116a4c95d63623a967edf2f3456c90ebbf964e6f  SOBONKR12A58A7A7E0   \n",
       "7   45544491ccfcdc0b0803c34f201a6287ed4e30f8  SOBONKR12A58A7A7E0   \n",
       "8   e701a24d9b6c59f5ac37ab28462ca82470e27cfb  SOBONKR12A58A7A7E0   \n",
       "9   edc8b7b1fd592a3b69c3d823a742e1a064abec95  SOBONKR12A58A7A7E0   \n",
       "10  fb41d1c374d093ab643ef3bcd70eeb258d479076  SOBONKR12A58A7A7E0   \n",
       "\n",
       "             title  listen_count  watched  pd_watched  \n",
       "0   You're The One             2        1           1  \n",
       "1   You're The One             0        0           0  \n",
       "2   You're The One             0        0           0  \n",
       "3   You're The One             0        0           0  \n",
       "4   You're The One             0        0           0  \n",
       "5   You're The One             0        0           0  \n",
       "6   You're The One            17        1           1  \n",
       "7   You're The One             0        0           0  \n",
       "8   You're The One            68        1           1  \n",
       "9   You're The One             0        0           0  \n",
       "10  You're The One             1        1           1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Binarizer\n",
    "\n",
    "bn = Binarizer(threshold=0.9)\n",
    "pd_watched = bn.transform([popsong_df['listen_count']])[0]\n",
    "popsong_df['pd_watched'] = pd_watched\n",
    "popsong_df.head(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rounding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>pop_percent</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>it_01345</td>\n",
       "      <td>0.98324</td>\n",
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       "      <th>1</th>\n",
       "      <td>it_03431</td>\n",
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       "    </tr>\n",
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       "      <td>it_01298</td>\n",
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       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>it_90120</td>\n",
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       "    </tr>\n",
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       "      <th>6</th>\n",
       "      <td>it_10123</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    item_id  pop_percent\n",
       "0  it_01345      0.98324\n",
       "1  it_03431      0.56123\n",
       "2  it_04572      0.12098\n",
       "3  it_98021      0.35476\n",
       "4  it_01298      0.92101\n",
       "5  it_90120      0.81212\n",
       "6  it_10123      0.56502"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_popularity = pd.read_csv('datasets/item_popularity.csv', encoding='utf-8')\n",
    "items_popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>35</td>\n",
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       "      <th>4</th>\n",
       "      <td>it_01298</td>\n",
       "      <td>0.92101</td>\n",
       "      <td>9</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>it_90120</td>\n",
       "      <td>0.81212</td>\n",
       "      <td>8</td>\n",
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       "      <th>6</th>\n",
       "      <td>it_10123</td>\n",
       "      <td>0.56502</td>\n",
       "      <td>6</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    item_id  pop_percent  popularity_scale_10  popularity_scale_100\n",
       "0  it_01345      0.98324                   10                    98\n",
       "1  it_03431      0.56123                    6                    56\n",
       "2  it_04572      0.12098                    1                    12\n",
       "3  it_98021      0.35476                    4                    35\n",
       "4  it_01298      0.92101                    9                    92\n",
       "5  it_90120      0.81212                    8                    81\n",
       "6  it_10123      0.56502                    6                    57"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_popularity['popularity_scale_10'] = np.array(np.round((items_popularity['pop_percent'] * 10)), dtype='int')\n",
    "items_popularity['popularity_scale_100'] = np.array(np.round((items_popularity['pop_percent'] * 100)), dtype='int')\n",
    "items_popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attack  Defense\n",
       "0      49       49\n",
       "1      62       63\n",
       "2      82       83\n",
       "3     100      123\n",
       "4      52       43"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atk_def = poke_df[['Attack', 'Defense']]\n",
    "atk_def.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    49.,     49.,   2401.,   2401.,   2401.],\n",
       "       [    62.,     63.,   3844.,   3906.,   3969.],\n",
       "       [    82.,     83.,   6724.,   6806.,   6889.],\n",
       "       ..., \n",
       "       [   110.,     60.,  12100.,   6600.,   3600.],\n",
       "       [   160.,     60.,  25600.,   9600.,   3600.],\n",
       "       [   110.,    120.,  12100.,  13200.,  14400.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "pf = PolynomialFeatures(degree=2, interaction_only=False, include_bias=False)\n",
    "res = pf.fit_transform(atk_def)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attack_degree</th>\n",
       "      <th>Defense_degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attack_degree  Defense_degree\n",
       "0              1               0\n",
       "1              0               1\n",
       "2              2               0\n",
       "3              1               1\n",
       "4              0               2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(pf.powers_, columns=['Attack_degree', 'Defense_degree'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Attack^2</th>\n",
       "      <th>Attack x Defense</th>\n",
       "      <th>Defense^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3844.0</td>\n",
       "      <td>3906.0</td>\n",
       "      <td>3969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>82.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>6724.0</td>\n",
       "      <td>6806.0</td>\n",
       "      <td>6889.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>12300.0</td>\n",
       "      <td>15129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2704.0</td>\n",
       "      <td>2236.0</td>\n",
       "      <td>1849.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attack  Defense  Attack^2  Attack x Defense  Defense^2\n",
       "0    49.0     49.0    2401.0            2401.0     2401.0\n",
       "1    62.0     63.0    3844.0            3906.0     3969.0\n",
       "2    82.0     83.0    6724.0            6806.0     6889.0\n",
       "3   100.0    123.0   10000.0           12300.0    15129.0\n",
       "4    52.0     43.0    2704.0            2236.0     1849.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intr_features = pd.DataFrame(res, columns=['Attack', 'Defense', 'Attack^2', 'Attack x Defense', 'Defense^2'])\n",
    "intr_features.head(5)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transforming new data in the future (during predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attack  Defense\n",
       "0      95       75\n",
       "1     121      120\n",
       "2      77       60"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = pd.DataFrame([[95, 75],[121, 120], [77, 60]], \n",
    "                      columns=['Attack', 'Defense'])\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Attack^2</th>\n",
       "      <th>Attack x Defense</th>\n",
       "      <th>Defense^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9025.0</td>\n",
       "      <td>7125.0</td>\n",
       "      <td>5625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>14641.0</td>\n",
       "      <td>14520.0</td>\n",
       "      <td>14400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5929.0</td>\n",
       "      <td>4620.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attack  Defense  Attack^2  Attack x Defense  Defense^2\n",
       "0    95.0     75.0    9025.0            7125.0     5625.0\n",
       "1   121.0    120.0   14641.0           14520.0    14400.0\n",
       "2    77.0     60.0    5929.0            4620.0     3600.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_res = pf.transform(new_df)\n",
    "new_intr_features = pd.DataFrame(new_res, \n",
    "                                 columns=['Attack', 'Defense', \n",
    "                                          'Attack^2', 'Attack x Defense', 'Defense^2'])\n",
    "new_intr_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID.x</th>\n",
       "      <th>EmploymentField</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cef35615d61b202f1dc794ef2746df14</td>\n",
       "      <td>office and administrative support</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323e5a113644d18185c743c241407754</td>\n",
       "      <td>food and beverage</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b29a1027e5cd062e654a63764157461d</td>\n",
       "      <td>finance</td>\n",
       "      <td>19.0</td>\n",
       "      <td>48000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04a11e4bcb573a1261eb0d9948d32637</td>\n",
       "      <td>arts, entertainment, sports, or media</td>\n",
       "      <td>26.0</td>\n",
       "      <td>43000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9368291c93d5d5f5c8cdb1a575e18bec</td>\n",
       "      <td>education</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               ID.x                        EmploymentField  \\\n",
       "0  cef35615d61b202f1dc794ef2746df14      office and administrative support   \n",
       "1  323e5a113644d18185c743c241407754                      food and beverage   \n",
       "2  b29a1027e5cd062e654a63764157461d                                finance   \n",
       "3  04a11e4bcb573a1261eb0d9948d32637  arts, entertainment, sports, or media   \n",
       "4  9368291c93d5d5f5c8cdb1a575e18bec                              education   \n",
       "\n",
       "    Age   Income  \n",
       "0  28.0  32000.0  \n",
       "1  22.0  15000.0  \n",
       "2  19.0  48000.0  \n",
       "3  26.0  43000.0  \n",
       "4  20.0   6000.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcc_survey_df = pd.read_csv('datasets/fcc_2016_coder_survey_subset.csv', encoding='utf-8')\n",
    "fcc_survey_df[['ID.x', 'EmploymentField', 'Age', 'Income']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fixed-width binning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Developer age distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1d51313ffd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OzK4Hvmpm/Xj39OH9awwfkH5dm/m6eRerHkDHU5Gv7eY6O0lP1LU/7x5+KSIiIt0zgOSU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\n4JiZmVl1XOCYmZlZdf4DsMHxh+cvvgYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d512e978d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fcc_survey_df['Age'].hist(color='#A9C5D3')\n",
    "ax.set_title('Developer Age Histogram', fontsize=12)\n",
    "ax.set_xlabel('Age', fontsize=12)\n",
    "ax.set_ylabel('Frequency', fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binning based on rounding\n",
    "\n",
    "``` \n",
    "Age Range: Bin\n",
    "---------------\n",
    " 0 -  9  : 0\n",
    "10 - 19  : 1\n",
    "20 - 29  : 2\n",
    "30 - 39  : 3\n",
    "40 - 49  : 4\n",
    "50 - 59  : 5\n",
    "60 - 69  : 6\n",
    "  ... and so on\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID.x</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age_bin_round</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>6a02aa4618c99fdb3e24de522a099431</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>f0e5e47278c5f248fe861c5f7214c07a</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>6e14f6d0779b7e424fa3fdd9e4bd3bf9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
       "      <td>c2654c07dc929cdf3dad4d1aec4ffbb3</td>\n",
       "      <td>53.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>f07449fc9339b2e57703ec7886232523</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  ID.x   Age  Age_bin_round\n",
       "1071  6a02aa4618c99fdb3e24de522a099431  17.0            1.0\n",
       "1072  f0e5e47278c5f248fe861c5f7214c07a  38.0            3.0\n",
       "1073  6e14f6d0779b7e424fa3fdd9e4bd3bf9  21.0            2.0\n",
       "1074  c2654c07dc929cdf3dad4d1aec4ffbb3  53.0            5.0\n",
       "1075  f07449fc9339b2e57703ec7886232523  35.0            3.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcc_survey_df['Age_bin_round'] = np.array(np.floor(np.array(fcc_survey_df['Age']) / 10.))\n",
    "fcc_survey_df[['ID.x', 'Age', 'Age_bin_round']].iloc[1071:1076]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binning based on custom ranges\n",
    "\n",
    "``` \n",
    "Age Range : Bin\n",
    "---------------\n",
    " 0 -  15  : 1\n",
    "16 -  30  : 2\n",
    "31 -  45  : 3\n",
    "46 -  60  : 4\n",
    "61 -  75  : 5\n",
    "75 - 100  : 6\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID.x</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age_bin_round</th>\n",
       "      <th>Age_bin_custom_range</th>\n",
       "      <th>Age_bin_custom_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>6a02aa4618c99fdb3e24de522a099431</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(15, 30]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>f0e5e47278c5f248fe861c5f7214c07a</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>(30, 45]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>6e14f6d0779b7e424fa3fdd9e4bd3bf9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>(15, 30]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
       "      <td>c2654c07dc929cdf3dad4d1aec4ffbb3</td>\n",
       "      <td>53.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>(45, 60]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>f07449fc9339b2e57703ec7886232523</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>(30, 45]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  ID.x   Age  Age_bin_round  \\\n",
       "1071  6a02aa4618c99fdb3e24de522a099431  17.0            1.0   \n",
       "1072  f0e5e47278c5f248fe861c5f7214c07a  38.0            3.0   \n",
       "1073  6e14f6d0779b7e424fa3fdd9e4bd3bf9  21.0            2.0   \n",
       "1074  c2654c07dc929cdf3dad4d1aec4ffbb3  53.0            5.0   \n",
       "1075  f07449fc9339b2e57703ec7886232523  35.0            3.0   \n",
       "\n",
       "     Age_bin_custom_range Age_bin_custom_label  \n",
       "1071             (15, 30]                    2  \n",
       "1072             (30, 45]                    3  \n",
       "1073             (15, 30]                    2  \n",
       "1074             (45, 60]                    4  \n",
       "1075             (30, 45]                    3  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_ranges = [0, 15, 30, 45, 60, 75, 100]\n",
    "bin_names = [1, 2, 3, 4, 5, 6]\n",
    "fcc_survey_df['Age_bin_custom_range'] = pd.cut(np.array(fcc_survey_df['Age']), \n",
    "                                               bins=bin_ranges)\n",
    "fcc_survey_df['Age_bin_custom_label'] = pd.cut(np.array(fcc_survey_df['Age']), \n",
    "                                               bins=bin_ranges, labels=bin_names)\n",
    "fcc_survey_df[['ID.x', 'Age', 'Age_bin_round', \n",
    "               'Age_bin_custom_range', 'Age_bin_custom_label']].iloc[1071:1076]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantile based binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID.x</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9368291c93d5d5f5c8cdb1a575e18bec</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dd0e77eab9270e4b67c19b0d6bbf621b</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7599c0aa0419b59fd11ffede98a3665d</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6dff182db452487f07a47596f314bddc</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9dc233f8ed1c6eb2432672ab4bb39249</td>\n",
       "      <td>33.0</td>\n",
       "      <td>80000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               ID.x   Age   Income\n",
       "4  9368291c93d5d5f5c8cdb1a575e18bec  20.0   6000.0\n",
       "5  dd0e77eab9270e4b67c19b0d6bbf621b  34.0  40000.0\n",
       "6  7599c0aa0419b59fd11ffede98a3665d  23.0  32000.0\n",
       "7  6dff182db452487f07a47596f314bddc  35.0  40000.0\n",
       "8  9dc233f8ed1c6eb2432672ab4bb39249  33.0  80000.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcc_survey_df[['ID.x', 'Age', 'Income']].iloc[4:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1d51322a208>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9geCmlIX0ou10/xvYOiFcfj8ocquIcIK5Y9h6Oa6IiIiUphfBFBz3unvF3j8wE0WTmR1N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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d513130710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fcc_survey_df['Income'].hist(bins=30, color='#A9C5D3')\n",
    "ax.set_title('Developer Income Histogram', fontsize=12)\n",
    "ax.set_xlabel('Developer Income', fontsize=12)\n",
    "ax.set_ylabel('Frequency', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00      6000.0\n",
       "0.25     20000.0\n",
       "0.50     37000.0\n",
       "0.75     60000.0\n",
       "1.00    200000.0\n",
       "Name: Income, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantile_list = [0, .25, .5, .75, 1.]\n",
    "quantiles = fcc_survey_df['Income'].quantile(quantile_list)\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1d51333fe80>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7BEmYclqe6uvr0w5BElAJeVTRJJLH8OHD0w5BEqaclpfq6mqqqqqora1NOxRJ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\nrAr/H0jcCPo/cBh4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d5132c65c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fcc_survey_df['Income'].hist(bins=30, color='#A9C5D3')\n",
    "\n",
    "for quantile in quantiles:\n",
    "    qvl = plt.axvline(quantile, color='r')\n",
    "ax.legend([qvl], ['Quantiles'], fontsize=10)\n",
    "\n",
    "ax.set_title('Developer Income Histogram with Quantiles', fontsize=12)\n",
    "ax.set_xlabel('Developer Income', fontsize=12)\n",
    "ax.set_ylabel('Frequency', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID.x</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Income_quantile_range</th>\n",
       "      <th>Income_quantile_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9368291c93d5d5f5c8cdb1a575e18bec</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>(5999.999, 20000.0]</td>\n",
       "      <td>0-25Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dd0e77eab9270e4b67c19b0d6bbf621b</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>(37000.0, 60000.0]</td>\n",
       "      <td>50-75Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7599c0aa0419b59fd11ffede98a3665d</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>(20000.0, 37000.0]</td>\n",
       "      <td>25-50Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6dff182db452487f07a47596f314bddc</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>(37000.0, 60000.0]</td>\n",
       "      <td>50-75Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9dc233f8ed1c6eb2432672ab4bb39249</td>\n",
       "      <td>33.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>(60000.0, 200000.0]</td>\n",
       "      <td>75-100Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               ID.x   Age   Income Income_quantile_range  \\\n",
       "4  9368291c93d5d5f5c8cdb1a575e18bec  20.0   6000.0   (5999.999, 20000.0]   \n",
       "5  dd0e77eab9270e4b67c19b0d6bbf621b  34.0  40000.0    (37000.0, 60000.0]   \n",
       "6  7599c0aa0419b59fd11ffede98a3665d  23.0  32000.0    (20000.0, 37000.0]   \n",
       "7  6dff182db452487f07a47596f314bddc  35.0  40000.0    (37000.0, 60000.0]   \n",
       "8  9dc233f8ed1c6eb2432672ab4bb39249  33.0  80000.0   (60000.0, 200000.0]   \n",
       "\n",
       "  Income_quantile_label  \n",
       "4                 0-25Q  \n",
       "5                50-75Q  \n",
       "6                25-50Q  \n",
       "7                50-75Q  \n",
       "8               75-100Q  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantile_labels = ['0-25Q', '25-50Q', '50-75Q', '75-100Q']\n",
    "fcc_survey_df['Income_quantile_range'] = pd.qcut(fcc_survey_df['Income'], \n",
    "                                                 q=quantile_list)\n",
    "fcc_survey_df['Income_quantile_label'] = pd.qcut(fcc_survey_df['Income'], \n",
    "                                                 q=quantile_list, labels=quantile_labels)\n",
    "fcc_survey_df[['ID.x', 'Age', 'Income', \n",
    "               'Income_quantile_range', 'Income_quantile_label']].iloc[4:9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mathematical Transformations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID.x</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Income_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9368291c93d5d5f5c8cdb1a575e18bec</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8.699681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dd0e77eab9270e4b67c19b0d6bbf621b</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>10.596660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7599c0aa0419b59fd11ffede98a3665d</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>10.373522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6dff182db452487f07a47596f314bddc</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>10.596660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9dc233f8ed1c6eb2432672ab4bb39249</td>\n",
       "      <td>33.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>11.289794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               ID.x   Age   Income  Income_log\n",
       "4  9368291c93d5d5f5c8cdb1a575e18bec  20.0   6000.0    8.699681\n",
       "5  dd0e77eab9270e4b67c19b0d6bbf621b  34.0  40000.0   10.596660\n",
       "6  7599c0aa0419b59fd11ffede98a3665d  23.0  32000.0   10.373522\n",
       "7  6dff182db452487f07a47596f314bddc  35.0  40000.0   10.596660\n",
       "8  9dc233f8ed1c6eb2432672ab4bb39249  33.0  80000.0   11.289794"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcc_survey_df['Income_log'] = np.log((1+ fcc_survey_df['Income']))\n",
    "fcc_survey_df[['ID.x', 'Age', 'Income', 'Income_log']].iloc[4:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1d5134d9780>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2YP9Tyjoeij0ruSB4/xr2ss4fU9qtnElup9K3kfIrVUTOwJ4tKbT4L+bE8oOcL8hhHhtJ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\nXxe/cKaNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d51341b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "income_log_mean = np.round(np.mean(fcc_survey_df['Income_log']), 2)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fcc_survey_df['Income_log'].hist(bins=30, color='#A9C5D3')\n",
    "plt.axvline(income_log_mean, color='r')\n",
    "ax.set_title('Developer Income Histogram after Log Transform', fontsize=12)\n",
    "ax.set_xlabel('Developer Income (log scale)', fontsize=12)\n",
    "ax.set_ylabel('Frequency', fontsize=12)\n",
    "ax.text(11.5, 450, r'$\\mu$='+str(income_log_mean), fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box–Cox transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal lambda value: 0.117991239456\n"
     ]
    }
   ],
   "source": [
    "# get optimal lambda value from non null income values\n",
    "income = np.array(fcc_survey_df['Income'])\n",
    "income_clean = income[~np.isnan(income)]\n",
    "l, opt_lambda = spstats.boxcox(income_clean)\n",
    "print('Optimal lambda value:', opt_lambda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\scipy\\stats\\morestats.py:1030: RuntimeWarning: invalid value encountered in less_equal\n",
      "  if any(x <= 0):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID.x</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Income_log</th>\n",
       "      <th>Income_boxcox_lambda_0</th>\n",
       "      <th>Income_boxcox_lambda_opt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9368291c93d5d5f5c8cdb1a575e18bec</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8.699681</td>\n",
       "      <td>8.699681</td>\n",
       "      <td>15.180668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dd0e77eab9270e4b67c19b0d6bbf621b</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>10.596660</td>\n",
       "      <td>10.596660</td>\n",
       "      <td>21.115342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7599c0aa0419b59fd11ffede98a3665d</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>10.373522</td>\n",
       "      <td>10.373522</td>\n",
       "      <td>20.346420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6dff182db452487f07a47596f314bddc</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>10.596660</td>\n",
       "      <td>10.596660</td>\n",
       "      <td>21.115342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9dc233f8ed1c6eb2432672ab4bb39249</td>\n",
       "      <td>33.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>11.289794</td>\n",
       "      <td>11.289794</td>\n",
       "      <td>23.637131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               ID.x   Age   Income  Income_log  \\\n",
       "4  9368291c93d5d5f5c8cdb1a575e18bec  20.0   6000.0    8.699681   \n",
       "5  dd0e77eab9270e4b67c19b0d6bbf621b  34.0  40000.0   10.596660   \n",
       "6  7599c0aa0419b59fd11ffede98a3665d  23.0  32000.0   10.373522   \n",
       "7  6dff182db452487f07a47596f314bddc  35.0  40000.0   10.596660   \n",
       "8  9dc233f8ed1c6eb2432672ab4bb39249  33.0  80000.0   11.289794   \n",
       "\n",
       "   Income_boxcox_lambda_0  Income_boxcox_lambda_opt  \n",
       "4                8.699681                 15.180668  \n",
       "5               10.596660                 21.115342  \n",
       "6               10.373522                 20.346420  \n",
       "7               10.596660                 21.115342  \n",
       "8               11.289794                 23.637131  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcc_survey_df['Income_boxcox_lambda_0'] = spstats.boxcox((1+fcc_survey_df['Income']), \n",
    "                                                         lmbda=0)\n",
    "fcc_survey_df['Income_boxcox_lambda_opt'] = spstats.boxcox(fcc_survey_df['Income'], \n",
    "                                                           lmbda=opt_lambda)\n",
    "fcc_survey_df[['ID.x', 'Age', 'Income', 'Income_log', \n",
    "               'Income_boxcox_lambda_0', 'Income_boxcox_lambda_opt']].iloc[4:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1d5147b4358>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CemzU0KhD8IZuE5bmITw+V1wQkQNFpLSzQpcDLxljCmMDjDGvAWuxFZiYc4HG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d513443320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "income_boxcox_mean = np.round(np.mean(fcc_survey_df['Income_boxcox_lambda_opt']), 2)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fcc_survey_df['Income_boxcox_lambda_opt'].hist(bins=30, color='#A9C5D3')\n",
    "plt.axvline(income_boxcox_mean, color='r')\n",
    "ax.set_title('Developer Income Histogram after Box–Cox Transform', fontsize=12)\n",
    "ax.set_xlabel('Developer Income (Box–Cox transform)', fontsize=12)\n",
    "ax.set_ylabel('Frequency', fontsize=12)\n",
    "ax.text(24, 450, r'$\\mu$='+str(income_boxcox_mean), fontsize=10)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
